The Impact of Fundamentals on Volatility Measures of Agricultural Substitutes
Bibliographic record
Abstract
Abstract This study builds upon the existing literature on the Working curve and backwardation to explore the impact of storage regimes on the volatility measures of substitute agricultural commodity markets. We investigate the impact of commodity fundamentals (storage regime and stocks-to-use ratio), commodity-specific financial variables (options hedging pressure-long and -short), world economic activity, market-wide volatility index, seasonality, and time-to-maturity on nearby and deferred implied volatility (IV) series of selected commodity pairs of corn-soybean and winter wheat-spring wheat. Our work confirms that, in some cases, grain and oilseed IV derived from options premia respond to shocks in commodity (and substitute commodity) fundamentals which are in line with the behaviour of volatility in futures markets. Own-storage regime effects on price variability are stronger in the selected markets, while spillover effects from substitute commodity storage regimes show a modest impact on volatilities. We also find some evidence for the stocks-to-use ratio of both corn and soybean to impact both their own and each other’s IV, while options hedging pressure has some impact only on wheat IVs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".